Abstract: The advent of social networking technologies has been
met with mixed reactions in academic and corporate circles around
the world. This study explored the influence of social network in
current era, the relation being maintained between the Social
networking site and its user by the extent of use, benefits and latest
technologies. The study followed a descriptive research design
wherein a questionnaire was used as the main research tool. The data
collected was analyzed using SPSS 16. Data was gathered from 1205
users and analyzed in accordance with the objectives of the study.
The analysis of the results seem to suggest that the majority of users
were mainly using Facebook, despite of concerns raised about the
disclosure of personal information on social network sites, users
continue to disclose huge quantity of personal information, they find
that reading privacy policy is time consuming and changes made can
result into improper settings.
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.